TAT hitchhiker selection expanded to folding helpers, multimeric interactions and combinations with protein fragment complementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The twin-arginine translocation (TAT) pathway of the bacterial cytoplasmic membrane mediates translocation only of proteins that accomplished a native-like conformation. We deploy this feature in modular selection systems for directed evolution, in which folding helpers as well as dimeric or oligomeric protein-protein interactions enable TAT-dependent translocation of the resistance marker TEM β-lactamase (βL). Specifically, we demonstrate and analyze selection of (i) enhancers for folding by direct TAT translocation selection of a target protein interposed between the TorA signal sequence and βL, (ii) dimeric or oligomeric protein-protein interactions by hitchhiker translocation (HiT) selection of proteins fused to the TorA signal sequence and to the βL, respectively and (iii) heterotrimeric protein-protein interactions by combining HiT with protein fragment complementation selection of proteins fused to two split βL fragments and TorA, respectively. The lactamase fragments were additionally engineered for improved activity and stability. Applicability was benchmarked with interaction partners of known affinity and multimerization whereby cellular fitness correlated well with biophysical protein properties. Ultimately, the HiT selection was employed to identify peptides, which specifically bind to leukemia- and melanoma-relevant target proteins (MITF and ETO) by coiled-coil or tetra-helix-bundle formation with high affinity. The various versions of TAT selection led to inhibiting peptides (iPEPs) of disease-promoting interactions and enabled so far difficult to achieve selections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it